Pub Date : 2018-09-01DOI: 10.1109/ETFA.2018.8502470
Laurin Prenzel, Julien Provost
Despite several architectural advantages for the challenges of future manufacturing systems, the IEC 61499 standard is currently not widely accepted by industry. One advantage of the IEC 61499 is the concept of downtimeless system evolution. An extension of this, dynamic software updating, which allows switching out running processes and deal with unplanned changes, is readily available in the programming language Erlang. This paper investigates the real-time performance of an asynchronous, parallel IEC 61499 basic function block implementation in Erlang, a functional programming language with a soft real-time, concurrent runtime environment. As a result, although hard real-time performance is not guaranteed and the runtime environment is executed on top of a regular operating system, the performance is consistent and promising for future implementations and extensions.
{"title":"Implementation and Evaluation of IEC 61499 Basic Function Blocks in Erlang","authors":"Laurin Prenzel, Julien Provost","doi":"10.1109/ETFA.2018.8502470","DOIUrl":"https://doi.org/10.1109/ETFA.2018.8502470","url":null,"abstract":"Despite several architectural advantages for the challenges of future manufacturing systems, the IEC 61499 standard is currently not widely accepted by industry. One advantage of the IEC 61499 is the concept of downtimeless system evolution. An extension of this, dynamic software updating, which allows switching out running processes and deal with unplanned changes, is readily available in the programming language Erlang. This paper investigates the real-time performance of an asynchronous, parallel IEC 61499 basic function block implementation in Erlang, a functional programming language with a soft real-time, concurrent runtime environment. As a result, although hard real-time performance is not guaranteed and the runtime environment is executed on top of a regular operating system, the performance is consistent and promising for future implementations and extensions.","PeriodicalId":6566,"journal":{"name":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"47 1","pages":"123-130"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81089004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ETFA.2018.8502498
Tomasz Kloda, A. Bertout, Y. Sorel
A data chain is a sequence of periodic realtime communicating tasks that are processing the data from sensors up to actuators. It determines an order in which the tasks propagate data but not in which they are executed: inter-task communication and scheduling are independent. In this paper, we focus on the latency computation, considered as the time elapsed from getting the data from an input and processing it to an output of a data chain. We propose a method for the worst-case latency calculation of periodic tasks' data chains executed by a partitioned fixed-priority preemptive scheduler upon a multiprocessor platform. As far as we know, there is no such formal approach based on closed-form expression for communicating real-time tasks.
{"title":"Latency analysis for data chains of real-time periodic tasks","authors":"Tomasz Kloda, A. Bertout, Y. Sorel","doi":"10.1109/ETFA.2018.8502498","DOIUrl":"https://doi.org/10.1109/ETFA.2018.8502498","url":null,"abstract":"A data chain is a sequence of periodic realtime communicating tasks that are processing the data from sensors up to actuators. It determines an order in which the tasks propagate data but not in which they are executed: inter-task communication and scheduling are independent. In this paper, we focus on the latency computation, considered as the time elapsed from getting the data from an input and processing it to an output of a data chain. We propose a method for the worst-case latency calculation of periodic tasks' data chains executed by a partitioned fixed-priority preemptive scheduler upon a multiprocessor platform. As far as we know, there is no such formal approach based on closed-form expression for communicating real-time tasks.","PeriodicalId":6566,"journal":{"name":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"370 1","pages":"360-367"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80459256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ETFA.2018.8502602
A. Chaves, Ricardo Maia, Carlos Belchio, R. Araújo, G. Gouveia
Current smart factories, and technologies such as cyber-physical systems and Internet of Things have transformed the working environments, that now incorporate digital machines with information technology. Integrated automated systems require being accurate and dependable even though operating in a non-predictable environment. The needs for testing, quality assurance, and maintenance for such types of systems are continuously increasing, and testing frameworks are often used to execute automated tests on those systems. With the objective of providing a support and enabling infrastructure for testing cyber-physical systems in closed-loop, it was designed and developed the KhronoSim testing platform. This paper presents the KhronoSim platform and architecture. It is modular, extensible and usable in multiple application domains. It features real-time control and enables the joint and flexible integration of simulation models and physical components or subsystems to build a closed-loop test environment. Furthermore, the paper presents a case study, displaying the flexibility and modularity of the KhronoSim platform and its capacity of integrating solutions from external sources, simulators, communication protocols, power sources, electrical inputs/outputs, demonstrating that it is a robust platform that is useful for testing and simulation of complex systems, yet allowing the use of physical and virtual systems alike.
{"title":"KhronoSim: A Platform for Complex Systems Simulation and Testing","authors":"A. Chaves, Ricardo Maia, Carlos Belchio, R. Araújo, G. Gouveia","doi":"10.1109/ETFA.2018.8502602","DOIUrl":"https://doi.org/10.1109/ETFA.2018.8502602","url":null,"abstract":"Current smart factories, and technologies such as cyber-physical systems and Internet of Things have transformed the working environments, that now incorporate digital machines with information technology. Integrated automated systems require being accurate and dependable even though operating in a non-predictable environment. The needs for testing, quality assurance, and maintenance for such types of systems are continuously increasing, and testing frameworks are often used to execute automated tests on those systems. With the objective of providing a support and enabling infrastructure for testing cyber-physical systems in closed-loop, it was designed and developed the KhronoSim testing platform. This paper presents the KhronoSim platform and architecture. It is modular, extensible and usable in multiple application domains. It features real-time control and enables the joint and flexible integration of simulation models and physical components or subsystems to build a closed-loop test environment. Furthermore, the paper presents a case study, displaying the flexibility and modularity of the KhronoSim platform and its capacity of integrating solutions from external sources, simulators, communication protocols, power sources, electrical inputs/outputs, demonstrating that it is a robust platform that is useful for testing and simulation of complex systems, yet allowing the use of physical and virtual systems alike.","PeriodicalId":6566,"journal":{"name":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"38 1","pages":"131-138"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87089124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ETFA.2018.8502569
Kee Jin Lee
Online machine learning has become increasingly important recently as more and more machines are being connected and data is being sent to the decision making node in real time. Traditional batch based machine learning is no longer suitable for such streaming data scenario. Here, an online classification algorithm to classify good and defective product, under imbalance streaming environment, is proposed. The proposed method exploits the assumption that different classes should be far away from each other. Even when the raw data might appear to be close, the algorithm learns and projects them into some specific manifold where different classes are far from each other. The algorithm classifies good and defective product in an imbalanced environment where good product outweighs defective product. The algorithm uses only single pass of the data, where the data is used once and then discarded. The approach is then being validated using industry data and the result indicates better performance in term of G-Mean and F1-score.
{"title":"Online Class Imbalance Learning for Quality Estimation in Manufacturing","authors":"Kee Jin Lee","doi":"10.1109/ETFA.2018.8502569","DOIUrl":"https://doi.org/10.1109/ETFA.2018.8502569","url":null,"abstract":"Online machine learning has become increasingly important recently as more and more machines are being connected and data is being sent to the decision making node in real time. Traditional batch based machine learning is no longer suitable for such streaming data scenario. Here, an online classification algorithm to classify good and defective product, under imbalance streaming environment, is proposed. The proposed method exploits the assumption that different classes should be far away from each other. Even when the raw data might appear to be close, the algorithm learns and projects them into some specific manifold where different classes are far from each other. The algorithm classifies good and defective product in an imbalanced environment where good product outweighs defective product. The algorithm uses only single pass of the data, where the data is used once and then discarded. The approach is then being validated using industry data and the result indicates better performance in term of G-Mean and F1-score.","PeriodicalId":6566,"journal":{"name":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"1 1","pages":"1007-1014"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87752741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ETFA.2018.8502542
M. Biagi, L. Carnevali, Kumiko Tadano, E. Vicario
Agile production systems face major issues in satisfying fickle market needs in highly demand-driven industry sectors, such as electronics and mechatronics. In this context, the time needed to complete the production of an item tends to be highly variable, and online estimation of the remaining completion time may suffer the lack of adequate sensor data, especially in existing manufacturing systems. To solve this issue, we propose a new analytical technique for the evaluation of an upper and a lower stochastic bound on the remaining completion time of a product, considering an assembly line made of sequential workstations with transfer blocking and buffer capacity. The approach notably encompasses service times with non-Markovian distribution, and avoids the limitation of existing works requiring the system to be at steady state at the inspection time. The technique is experimented on a case study and validated through simulation, providing an empirical analysis of its complexity.
{"title":"Evaluation of stochastic bounds on the remaining completion time of products in a buffered sequential workflow","authors":"M. Biagi, L. Carnevali, Kumiko Tadano, E. Vicario","doi":"10.1109/ETFA.2018.8502542","DOIUrl":"https://doi.org/10.1109/ETFA.2018.8502542","url":null,"abstract":"Agile production systems face major issues in satisfying fickle market needs in highly demand-driven industry sectors, such as electronics and mechatronics. In this context, the time needed to complete the production of an item tends to be highly variable, and online estimation of the remaining completion time may suffer the lack of adequate sensor data, especially in existing manufacturing systems. To solve this issue, we propose a new analytical technique for the evaluation of an upper and a lower stochastic bound on the remaining completion time of a product, considering an assembly line made of sequential workstations with transfer blocking and buffer capacity. The approach notably encompasses service times with non-Markovian distribution, and avoids the limitation of existing works requiring the system to be at steady state at the inspection time. The technique is experimented on a case study and validated through simulation, providing an empirical analysis of its complexity.","PeriodicalId":6566,"journal":{"name":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"49 1","pages":"456-463"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90335063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ETFA.2018.8502591
Marco Giacomelli, M. Faroni, D. Gorni, Alberto Marini, L. Simoni, A. Visioli
In this paper, a Model Predictive Control approach for the velocity control of operator-in-the loop overhead cranes is proposed. The operator can select the maximum position overshoot as a tuning parameter for the method. Simulations provide a comparison between the proposed method and the well known Zero Vibration input shaping technique, showing its effectiveness in controlling the payload oscillations.
{"title":"Model Predictive Control for operator-in-the-loop overhead cranes","authors":"Marco Giacomelli, M. Faroni, D. Gorni, Alberto Marini, L. Simoni, A. Visioli","doi":"10.1109/ETFA.2018.8502591","DOIUrl":"https://doi.org/10.1109/ETFA.2018.8502591","url":null,"abstract":"In this paper, a Model Predictive Control approach for the velocity control of operator-in-the loop overhead cranes is proposed. The operator can select the maximum position overshoot as a tuning parameter for the method. Simulations provide a comparison between the proposed method and the well known Zero Vibration input shaping technique, showing its effectiveness in controlling the payload oscillations.","PeriodicalId":6566,"journal":{"name":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"5 1","pages":"589-596"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83422737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ETFA.2018.8502588
Jonas Wassermann, Vojtach Vonesek, A. Vick
Recent developments in connected industries and internet of things identified demands for flexible reconfiguration and reprogramming of robots and machine tools. The demand for reconfiguration comes from changes in production process ordering or individual products; the demand for reprogramming comes from changing workplace organization and material flow. Yet these reconfiguration and reprogramming is often characterized by constants for a specific use-case in terms of precomputed trajectories. In this paper, we present an approach of monitoring the robot's workspace and using an online replanning of motion. We present a toolchain that is available and ready to use for a big class of industrial robots that have a position setpoint control interface. The feasibility is demonstrated in small laboratory experiments with a modular industrial robot in a common human-robot interface scenario.
{"title":"Distributed Industrial Robot Control Using Environment Perception and Parallel Path Planning Cloud Services","authors":"Jonas Wassermann, Vojtach Vonesek, A. Vick","doi":"10.1109/ETFA.2018.8502588","DOIUrl":"https://doi.org/10.1109/ETFA.2018.8502588","url":null,"abstract":"Recent developments in connected industries and internet of things identified demands for flexible reconfiguration and reprogramming of robots and machine tools. The demand for reconfiguration comes from changes in production process ordering or individual products; the demand for reprogramming comes from changing workplace organization and material flow. Yet these reconfiguration and reprogramming is often characterized by constants for a specific use-case in terms of precomputed trajectories. In this paper, we present an approach of monitoring the robot's workspace and using an online replanning of motion. We present a toolchain that is available and ready to use for a big class of industrial robots that have a position setpoint control interface. The feasibility is demonstrated in small laboratory experiments with a modular industrial robot in a common human-robot interface scenario.","PeriodicalId":6566,"journal":{"name":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"197 1","pages":"1263-1266"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83511381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ETFA.2018.8502565
Roberto Nardone, G. Tommasi, N. Mazzocca, A. Pironti, V. Vittorini
This paper aims at defining a model-driven approach for the diagnosability analysis of discrete event systems (DES). The proposed approach can be adopted during the design of modern control systems, in which many sensors and actuators are employed and the diagnosability of faults within a certain delay could be an issue. The proposal represents a first step towards an automatic model-driven process which derive formal models from a complete high-level specification of DESs. The specification activity of our approach relies on the Dynamic STate Machine (DSTM) formalism, a new language that extends state machines with dynamic instantiation, interrupts and asynchronous communication. The paper will describe how we can automatically derive Petri net and Promela models from the high-level DSTM specification. The former model can be used to apply diagnosability analysis approaches proposed in the DES community, while the latter can be used to apply model checking techniques. An application of the proposed model-driven approach is described by deriving both a PN and a Promela model for the well-known railway level crossing benchmark.
{"title":"Automatic generation of formal models for diagnosability of DES","authors":"Roberto Nardone, G. Tommasi, N. Mazzocca, A. Pironti, V. Vittorini","doi":"10.1109/ETFA.2018.8502565","DOIUrl":"https://doi.org/10.1109/ETFA.2018.8502565","url":null,"abstract":"This paper aims at defining a model-driven approach for the diagnosability analysis of discrete event systems (DES). The proposed approach can be adopted during the design of modern control systems, in which many sensors and actuators are employed and the diagnosability of faults within a certain delay could be an issue. The proposal represents a first step towards an automatic model-driven process which derive formal models from a complete high-level specification of DESs. The specification activity of our approach relies on the Dynamic STate Machine (DSTM) formalism, a new language that extends state machines with dynamic instantiation, interrupts and asynchronous communication. The paper will describe how we can automatically derive Petri net and Promela models from the high-level DSTM specification. The former model can be used to apply diagnosability analysis approaches proposed in the DES community, while the latter can be used to apply model checking techniques. An application of the proposed model-driven approach is described by deriving both a PN and a Promela model for the well-known railway level crossing benchmark.","PeriodicalId":6566,"journal":{"name":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"40 1","pages":"43-48"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82002180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ETFA.2018.8502663
S. Panda, Tizian Schroder, Lukasz Wisniewski, C. Diedrich
Current manufacturing and production systems are becoming more flexible and adaptable. Such systems demand rapid changeover and short configuration time of machines. With the adaptation of IT systems in the automation industry, machines and devices will have self-descriptive semantic device data description to represent their data elements and services. This paper proposes an approach to integrate new machines and devices into an existing production system without any manual intervention. The proposed concept provides a Plug & Produce system architecture and evaluates its capabilities based on OPC UA. The concept also describes an easy and fast integration of the digital representation of new field devices into an existing production system.
{"title":"Plug&Produce Integration of Components into OPC UA based data-space","authors":"S. Panda, Tizian Schroder, Lukasz Wisniewski, C. Diedrich","doi":"10.1109/ETFA.2018.8502663","DOIUrl":"https://doi.org/10.1109/ETFA.2018.8502663","url":null,"abstract":"Current manufacturing and production systems are becoming more flexible and adaptable. Such systems demand rapid changeover and short configuration time of machines. With the adaptation of IT systems in the automation industry, machines and devices will have self-descriptive semantic device data description to represent their data elements and services. This paper proposes an approach to integrate new machines and devices into an existing production system without any manual intervention. The proposed concept provides a Plug & Produce system architecture and evaluates its capabilities based on OPC UA. The concept also describes an easy and fast integration of the digital representation of new field devices into an existing production system.","PeriodicalId":6566,"journal":{"name":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"3 1","pages":"1095-1100"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85347081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-01DOI: 10.1109/ETFA.2018.8502459
A. Cervato, Chiara Corazzol, Luca Mattiello, M. Rampazzo
In this paper, the energy efficient control of carbon dioxide heat pump systems is discussed from an experimental point of view. The performance of this kind of systems strongly depends on the operating conditions and in particular on the cycle high pressure. Because of the limited knowledge of certain system parameters and the difficulty of developing and implementing effective models, the problem of determining the optimal value for the cycle high pressure that leads to the maximum system performance is here faced by means of a model-free approach. Specifically, an Extremum Seeking Control (ESC) scheme, which can search for the unknown or slowly varying optimum input with respect to a certain performance index, is adopted. In particular, a variable water flow rate heat pump unit was considered. In this scenario, the performances of the ESC were compared with those provided by other methods available in literature (e.g. Liao's model). Experimental tests show that the ESC scheme guarantees better performance.
{"title":"Maximizing CO2 Heat Pump Systems Performance via Extremum Seeking Control","authors":"A. Cervato, Chiara Corazzol, Luca Mattiello, M. Rampazzo","doi":"10.1109/ETFA.2018.8502459","DOIUrl":"https://doi.org/10.1109/ETFA.2018.8502459","url":null,"abstract":"In this paper, the energy efficient control of carbon dioxide heat pump systems is discussed from an experimental point of view. The performance of this kind of systems strongly depends on the operating conditions and in particular on the cycle high pressure. Because of the limited knowledge of certain system parameters and the difficulty of developing and implementing effective models, the problem of determining the optimal value for the cycle high pressure that leads to the maximum system performance is here faced by means of a model-free approach. Specifically, an Extremum Seeking Control (ESC) scheme, which can search for the unknown or slowly varying optimum input with respect to a certain performance index, is adopted. In particular, a variable water flow rate heat pump unit was considered. In this scenario, the performances of the ESC were compared with those provided by other methods available in literature (e.g. Liao's model). Experimental tests show that the ESC scheme guarantees better performance.","PeriodicalId":6566,"journal":{"name":"2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)","volume":"231 1","pages":"1328-1334"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91024794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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